{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stellargraph example: GraphSAGE on the CORA citation network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import NetworkX and stellar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import GraphSAGENodeGenerator\n",
    "from stellargraph.layer import GraphSAGE\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the CORA network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgelist = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.cites\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=[\"target\", \"source\"],\n",
    ")\n",
    "edgelist[\"label\"] = \"cites\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(Gnx, \"paper\", \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the features and subject for the nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "column_names = feature_names + [\"subject\"]\n",
    "node_data = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.content\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=column_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Case_Based',\n",
       " 'Genetic_Algorithms',\n",
       " 'Neural_Networks',\n",
       " 'Probabilistic_Methods',\n",
       " 'Reinforcement_Learning',\n",
       " 'Rule_Learning',\n",
       " 'Theory'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(node_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data, train_size=0.1, test_size=None, stratify=node_data[\"subject\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Rule_Learning': 18,\n",
       "         'Probabilistic_Methods': 42,\n",
       "         'Genetic_Algorithms': 42,\n",
       "         'Theory': 35,\n",
       "         'Neural_Networks': 81,\n",
       "         'Reinforcement_Learning': 22,\n",
       "         'Case_Based': 30})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict(\"records\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GraphSAGE model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(Gnx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 2708, Edges: 5278\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5278]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `GraphSAGENodeGenerator` as we are predicting node attributes with a GraphSAGE model.\n",
    "\n",
    "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-level model with 10 nodes sampled in the first layer, and 5 in the second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "num_samples = [10, 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `GraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = GraphSAGENodeGenerator(G, batch_size, num_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer.\n",
    " * The `bias` and `dropout` are internal parameters of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphsage_model = GraphSAGE(\n",
    "    layer_sizes=[32, 32], generator=generator, bias=True, dropout=0.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a model to predict the 7 categories using Keras softmax layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = graphsage_model.build()\n",
    "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=prediction)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "6/6 - 3s - loss: 1.8615 - acc: 0.2519 - val_loss: 1.7019 - val_acc: 0.3039\n",
      "Epoch 2/20\n",
      "6/6 - 3s - loss: 1.6065 - acc: 0.3963 - val_loss: 1.5400 - val_acc: 0.4094\n",
      "Epoch 3/20\n",
      "6/6 - 3s - loss: 1.4311 - acc: 0.6407 - val_loss: 1.3701 - val_acc: 0.6432\n",
      "Epoch 4/20\n",
      "6/6 - 3s - loss: 1.2864 - acc: 0.8185 - val_loss: 1.2441 - val_acc: 0.7375\n",
      "Epoch 5/20\n",
      "6/6 - 3s - loss: 1.1268 - acc: 0.8778 - val_loss: 1.1422 - val_acc: 0.7617\n",
      "Epoch 6/20\n",
      "6/6 - 3s - loss: 1.0006 - acc: 0.9333 - val_loss: 1.0626 - val_acc: 0.7662\n",
      "Epoch 7/20\n",
      "6/6 - 3s - loss: 0.8767 - acc: 0.9630 - val_loss: 0.9957 - val_acc: 0.7838\n",
      "Epoch 8/20\n",
      "6/6 - 3s - loss: 0.7909 - acc: 0.9556 - val_loss: 0.9444 - val_acc: 0.7834\n",
      "Epoch 9/20\n",
      "6/6 - 3s - loss: 0.7036 - acc: 0.9815 - val_loss: 0.8946 - val_acc: 0.7982\n",
      "Epoch 10/20\n",
      "6/6 - 3s - loss: 0.6046 - acc: 0.9926 - val_loss: 0.8591 - val_acc: 0.8023\n",
      "Epoch 11/20\n",
      "6/6 - 3s - loss: 0.5533 - acc: 0.9926 - val_loss: 0.8366 - val_acc: 0.7998\n",
      "Epoch 12/20\n",
      "6/6 - 3s - loss: 0.4997 - acc: 0.9889 - val_loss: 0.8055 - val_acc: 0.7970\n",
      "Epoch 13/20\n",
      "6/6 - 3s - loss: 0.4467 - acc: 0.9926 - val_loss: 0.7684 - val_acc: 0.8080\n",
      "Epoch 14/20\n",
      "6/6 - 3s - loss: 0.4014 - acc: 0.9963 - val_loss: 0.7512 - val_acc: 0.8093\n",
      "Epoch 15/20\n",
      "6/6 - 3s - loss: 0.3547 - acc: 1.0000 - val_loss: 0.7465 - val_acc: 0.8052\n",
      "Epoch 16/20\n",
      "6/6 - 3s - loss: 0.3427 - acc: 0.9963 - val_loss: 0.7377 - val_acc: 0.7974\n",
      "Epoch 17/20\n",
      "6/6 - 3s - loss: 0.3074 - acc: 0.9963 - val_loss: 0.7321 - val_acc: 0.7957\n",
      "Epoch 18/20\n",
      "6/6 - 3s - loss: 0.2715 - acc: 0.9926 - val_loss: 0.7198 - val_acc: 0.7945\n",
      "Epoch 19/20\n",
      "6/6 - 3s - loss: 0.2526 - acc: 0.9963 - val_loss: 0.7084 - val_acc: 0.8007\n",
      "Epoch 20/20\n",
      "6/6 - 3s - loss: 0.2465 - acc: 0.9926 - val_loss: 0.6972 - val_acc: 0.8043\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen, epochs=20, validation_data=test_gen, verbose=2, shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_history(history):\n",
    "    metrics = sorted(history.history.keys())\n",
    "    metrics = metrics[:len(metrics)//2]\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        plt.plot(history.history['val_' + m])\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'test'], loc='best')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QSin7aBDUli7XwYB7YP0bsG3R6cXTBsUxtH0Uf12yi8SUHBsLVEqpymkQ1KaRT0LsQGuayxRrjh0R4bkbuxPk78N987dQWKKnlCql3IsGQW3y9oUb3gG/YFgwBQpPAhAdEsCzE7qz89hJ7pu/mVKd1Uwp5UacOUPZbBFJEZFqx2cWkb4iUioiNzirFpdq2BRufAcyDsCn95ye2Wxk58Y8cU1nlu08wcOLt1Gu4xEppdyEM/cI5gBjqmsgIt7As8AyJ9bhenGDYcQTsOszWPvq6cW3XdaKP4xoz+JNyTy9ZKeeSaSUcgvOnKpylYjEnafZ74DFQF9n1WGbQb+D5PXwzePQvDe0HATAfcPbkl1QwuwfDhDawJf7R7S3uVCllKezrY9ARJoD1wGv16DtDBFJEJGE1NRU5xdXG0Rg/KsQHgcf3QY5xx2LhUev6sQNfWJ4afleZn9/wNYylVLKzs7il4CHjTHn7Tk1xswyxsQbY+KjoqJcUFotCQiFm+ZBUQ58dDuUWfMae3kJz1zfjTFdmvDUFztZtDHZ5kKVUp7MziCIBz4UkYPADcBrInKtjfU4R+POcM3LcHgNLH/i9GIfby9entSTwW0jeXjxVr7ecdy+GpVSHs22IDDGtDLGxBlj4oBFwN3GmE/sqsepuk+EvnfA2n/DujNHwvx9vHljah+6x4Tyuw8280Nimo1FKqU8lTNPH50PrAU6iEiyiEwXkbtE5C5nbdOtjf4HdLwavpoJq54/vTjI34d3butLq8gg7nw3gc2HM20sUinliaSuncIYHx9vEhIS7C7j4pSVwie/hW0LYcgD8KvHrE5lIOVkITe+sZas/BIW/mYgHZqE2FysUqo+EZGNxpj4ytbplcWu5O0D1/0Hek+D1f+Erx45fcFZdMMA5k3vT4CvF1Pf/pHD6fk2F6uU8hQaBK7m5W11Hvf/Lfz4Onz+eyi3xh9qERHIe9P7U1xWzi1vr+PEyUKbi1VKeQINAjuIwJh/WIeHNs215j0uKwWsoavn3t6PjNxipr79I5l5xTYXq5Sq7zQI7CICwx+3+gm2LYRFt52e1KZHizDenBbPwfR8bpuzgdyiUntrVUrVaxoEdrv8TzDmGdj1OXw4GUoKABjUJpJ/T+rF9iPZzHg3QYevVko5jQaBOxjwW2vu48Tl8P6N1pXIwKguTfi/Cd1Zsy9dh69WSjmNBoG76DMNrp8Fh9bAe9dBQRYAE/rEnB6++qHFW3X4aqVUrdMgcCfdJ8LEuXB0C8y9BvLSgTPDV/930xGeX/azzUUqpeobDQJ30+kamPQhpO2BOVeeHrX0vuFtmdQvltdW7uPD9YdtLlIpVZ9oELijdiPglkWQlQTvjIWsJESEp8Z3YUi7SP78yXZW760jw3ErpdyeBoG7ajUEbv3UOjz0zlhI34evtxev3dKbdtHB3D1vE3tO5NhdpVKqHtAgcGct+sJtn0NJPrxzJaTsJiTAl7dv60uAnze3v7OBlBy9+lgpdWk0CNxd0x5w21Lr8Zwr4eAPNA9rwOxpfcnIK+aOuQkUFOs1Bkqpi6dBUBdEd4Tbl0KDcOtsojX/olvzhrwyqRfbjmRz/4LNlOlppUqpi6RBUFc0agN3roCOV8GyR2HhVEa2bsBjV3Xm6x0neObLXXZXqJSqo5w5Mc1sEUkRke1VrL9FRLaKyDYRWSMiPZxVS70R0BAmvguj/ga7l8KsYdzeLp9pA1vy5uoDvLfukN0VKqXqIGfuEcwBxlSz/gAw1BjTDXgamOXEWuoPERh0L9z2BRTnIm+N4PGW2/lVx2j+8ul2VuxOsbtCpVQd47QgMMasAjKqWb/GGHNqXsZ1QIyzaqmXWg6C36yGZr3w/uQ3vBHxAd2aNODeDzax8+hJu6tTStUh7tJHMB34sqqVIjJDRBJEJCE1VS+kOi2kMdz6GQy6D99Ns/nI/yna+mfx6zkbOJ6tp5UqpWrG9iAQkSuwguDhqtoYY2YZY+KNMfFRUVGuK64u8PaBUU/DxPfwy0hksddMuhUm8Os5G8jTeQyUUjVgaxCISHfgLWC8MSbdzlrqvM7jYMZKfEKbMsvrH4xIncN9H2zU00qVUudlWxCISCzwX2CqMWaPXXXUK5Ft4Y7lSPeJ/NFnEVP2P8hzH6/FGA0DpVTVnHn66HxgLdBBRJJFZLqI3CUidzmaPA40Al4TkS0ikuCsWjyKXxBc9wZc9U8u99nBlK1T+ezLpXZXpZRyY1LX/lqMj483CQmaGTVRnpRA1txJBJVkkBj/F7pc/Tvr9FOllMcRkY3GmPjK1tneWaycx6tFPA3u/YGdft3psvExMj648/Q0mEopdYoGQT3XICya5vd+wWzviYTtXUTpK/GwbRHUsT1BpZTzaBB4gOjQIAbPeIFb+RuJ+YGweDq8Ox5StY9eKaVB4DHaNw5h5p1TmGT+zvM+Myg/ugVeHwTLn4DiPLvLU0rZSIPAg3RtHsp7dw7ivbKRXMNL5HW4Dr5/EV7tD7s+18NFSnkoDQIP07V5KO/f0Z/k4mBGHZjEiRs+Af+GsGAKvH8jpO+zu0SllIvVKAhE5Pci0lAsb4vIJhEZ5ezilHOcCoPcolKu/8KQNPErGP0POLwOXhsIK/4OJQV2l6mUcpGa7hH82hhzEhgFhANTgWecVpVyuophcPNbCSR1uA3u3QCdroH/PQuvDYA9X9tdplLKBWoaBKeuQroSeM8Ys6PCMlVHnQqDnMISbp61jqTSULjhbWtEU28/+GAizJ8MWYftLlUp5UQ1DYKNIrIMKwi+FpEQoNx5ZSlXscJgwJkwyMiH1kPhrh9gxJOwfwX8ux+seh5Ki+wuVynlBDUNgunATKCvMSYf8AVud1pVyqW6xZwJg0lvOsLAxw8G328dLmo3Er572jrddMcnUK5/AyhVn9Q0CAYCPxtjskRkCvAokO28spSrdYsJZd4d/TlZYIVBcma+tSI0Bm56D6YsBvGGj6bBG5fD7iV6uqlS9URNg+B1IN8xwfwDwD7gXadVpWzRPSbsdBjcPKtCGAC0HQF3r4Xr34SSfPhwMswaanUoayAoVafVNAhKjTVM6Xjg38aYV4EQ55Wl7HIqDLIrCwMvb+g+Ee5ZD9e+DoXZVofyW8MhcbkGglJ1VE2DIEdEHsE6bXSJiHhh9ROoeqh7TBjvO8LgrMNEp3j7QM/JcG8CjPsX5KbCvAkwezTsX6mBoFQdU9MguAkowrqe4DgQAzxX3QtEZLaIpIjI9irWi4i8IiKJIrJVRHpfUOXKqbrHhDFven+y8q0wOJJVyQVm3r7Q+1b43Ua46gXITrYGs5tzFRz83vVFK6UuSo2CwPHL/30gVESuBgqNMefrI5gDjKlm/VigneM2A6sfQrmRHi3OhMHNs9ZWHgZgnWHUdzr8bhOMfc4apmLOVTD3GutqZaWUW6vpEBMTgfXAjcBE4EcRuaG61xhjVgEZ1TQZD7xrLOuAMBFpWrOylav0aBHGexXCICkjv+rGvgHQfwb8fos1ZEXKLutw0XvXQ7LOKqeUu6rpoaE/Y11DMM0YcyvQD3jsErfdHEiq8DzZsUy5mZ6OMMjOL+GG/6xhz4nzzHLm2wAG3g2//wlGPgVHN1sdyu/faHUql5e5pnClVI3UNAi8jDEpFZ6nX8BrL5mIzBCRBBFJSE1NddVmVQU9W4Sx8K6BGAM3/mctmw5nnv9FfkFw2e/h/q0w/HFrr2DeBHi5B6x8BrKSzv8eSimnq+kv869E5GsRuU1EbgOWAEsvcdtHgBYVnsc4lv2CMWaWMSbeGBMfFRV1iZtVF6tjk4YsumsQYYG+3PLmj6zaU8NQ9g+BIQ/AA7vhhnegUVtY+Q94qZt12GjHJ1Ba7NzilVJVElPDU/1EZAJwmePpamPMxzV4TRzwhTGmayXrrgLuxRq/qD/wijGm3/neMz4+3iQk6PFmO6XkFDJt9gYSU3J48aaeXN292YW/SeYh2PI+bJ4HJ49AYCPoMQl6TYXojrVftFIeTkQ2GmPiK11X0yC4iI3OB4YBkcAJ4C84rj0wxvxHRAT4N9aZRfnA7caY8/6G1yBwD9kFJdwxdwMJhzJ5enxXpgxoeXFvVF4G+1bAprnw81IoL4WYftZpqV2uA//g2i1cKQ910UEgIjlAZQ0EMMaYhrVTYs1pELiPguIy7vlgE9/tTuFPo9pzzxVtsfL9IuWmwk/zYfN7kLYH/IKh6/XQexo07wOX8t5KeThb9gicRYPAvZSUlfPQoq18vPkI0we34s9XdsLL6xJ/YRsDST/Cpndhx8fW2EbRna3DRt1vgqBGtVO8Uh5Eg0A5VXm54akvdjJnzUGu792cZyd0x9e7lk4qKzwJ2xdboXB0E3j5Qocx0HOKNRCet0/tbEepeq66INCfInXJvLyEv1zTmYggP174Zg8nC0r49+TeBPh6X/qbBzSE+Nut24kdsPl92LoAdn0OwY2tPYReUyCqw6VvSykPpXsEqla9t/Ygj3+2g75xEbw1LZ6GAU4Ym7CsBPYus0Jh79dWB3PzPtDzFug6ARqE1f42larj9NCQcqnPfjrKHxdsoX3jEOb+uh9RIf7O21huqrWHsOV9SNkJPgHQ8WrodQu0GmoNna2U0iBQrrfy5xR+O28TjRv68970/rSICHTuBo2BY1usvYRtH0FhFjSMgZ6TrOsTGrVx7vaVcnMaBMoWGw9lcPs7G2jg58170/vTvrGL5jIqKbSuSdjyPuz7Dkw5xA6y9hLaj4GgSNfUoZQb0SBQttl9/CS3vr2eotJy3rm9L71jw11bwMmj1rUJWz6A9ERrWZNu0PoKaHMFxA60BslTqp7TIFC2SsrIZ8rbP5JysohnJnRjfE8bBpk1xhoFdd+3sG+ldZ1CeQl4+0PsACsUWl8BTbqDl8vGU1TKZTQIlO1Scgq5e94mEg5lcn3v5jw1vivB/jaevVyUC4fXWsNb7F9hdTSDNeZRq6FngiGsRfXvo1QdoUGg3EJpWTn/+i6Rf323l9iIQF6+uRc9WrjJqZ45x635lk8FQ+4Ja3lEmzOh0GoIBITaWqZSF0uDQLmV9QcyuP/DzaTkFPGn0R2YMaT1pQ9LUZuMsWZX27/CCoZDP1jDXIgXRLSG6E7WkBen7iNaW/M3K+XGNAiU28nOL+GRj7eydNtxBreN5IWJPYhuGGB3WZUrLYLkDXBgNaTssEIiY791NhKAtx9EtoeojmeHRFhL7W9QbkODQLklYwwLNiTx5Oc7aeDnzXM3dGd4p8Z2l1UzJQXWCKkpu86+ZR8+08Y3sEI4OG7N+0ADF585pRQaBMrNJabkct/8zew8dpLbBsUxc2zH2hmnyA6FJyH1Z6vzOWXXmfu8UzO9irXHEDsAWg6y7kNjbC1ZeQYNAuX2ikrLePbLn5n9wwE6NgnhX5N60c5VF6C5Ql46nNhunbZ6eC0krYfiXGtdaKwjGAZa1zVEdtBDSqrW2RYEIjIGeBnwBt4yxjxzzvpYYC4Q5mgz0xhT7VzIGgT124qfU/jTwp/ILSrlsas7c0v/2Eub7MZdlZVawXB4HRxeA4fWntlraBAOLQac2Wto2hN8/OytV9V5dk1V6Q3sAUYCycAGYJIxZmeFNrOAzcaY10WkM7DUGBNX3ftqENR/KTmFPLDwJ1bvTWNU58Y8O6E74UH1/BehMVYH9KlgOLzuzJXQPgHQPP7MhW8t+utZSuqC2TUfQT8g0Riz31HEh8B4YGeFNgY4Nd1lKHDUifWoOiI6JIC5t/dj9g8HePar3Yx9eTUv3tSTgW3q8cxkItbAeI3aWGMiAeSmOILBEQ7fvwirnwe/EGg9FNoOtybnCYu1t3ZV5zlzj+AGYIwx5g7H86lAf2PMvRXaNAWWAeFAEDDCGLOxkveaAcwAiI2N7XPo0CGn1Kzcz/Yj2dw3fzMH0vO4e1gb7h/RvvZmP6trCk/CgVWQuNy6ZSdZyyPbW4HQdji0vEzHTlKVsuvQUE2C4I+OGv4pIgOBt4Guxpw6QfuX9NCQ58krKuXJz3ewMCGZjk1CeO6GHnSL8fArfI2BtL1nQuHg91BWBD4NIG6wIxhGWHsY9bGPRV0wu4JgIPCEMWa04/kjAMaYf1RoswMrLJIcz/cDA4wxKZW8JaBB4BVXScUAABZxSURBVMmW7TjOo59sJz2vmDuHtOb+Ee3q7mmmta04Hw6tORMM6Xut5WEtz4RCqyHgX4/OxFIXxK4g8MHqLB4OHMHqLJ5sjNlRoc2XwAJjzBwR6QR8CzQ31RSlQeDZsgtK+PuSXSxISKJVZBDPTuhOv1YRdpflfjIPQuK31u3A/6xTVb18IDzOCofwOAhvefZjvdCtXrPz9NErgZewTg2dbYz5m4g8BSQYYz5znCn0JhCM1XH8kDFmWXXvqUGgAH5ITGPmf7eSlFHA1AEteXhsR3tHM3VnpcXW9Qv7V1hnImUegqxDUJB5druA0LODITwOwuIc9y3Ax4lTjiqn0wvKVL2UX1zK81/v4Z01B2jaMIC/Xd+NKzpE211W3VGYbYVC5kErGDIPVnh+2OpzOE0gpCkENLROZ/VtcJ77AKu/ouK9byD4N4QGYRAQZt37BmofhotoEKh6beOhTB5evJXElFyu79Wcx67uXP+vO3C28nLIPX52UGQdhqKT1lSgpYXWeEtV3VPD3yvefmdCoUH4mccBjufnPvYLssLDL8gKHd8g8NY9wZrQIFD1XlFpGa9+l8hrK/cRFujLk+O6cmW3JvXzqmR3ZwyUFVcSEPnWKbCFWVCQ5bjPrPC44rJsKMqu2fa8/axw8A0Ev8BzgqLCY79gCIyA4MaOW7R1HxjpEWGiQaA8xs6jJ3l48Va2HclmdJfGPD2+q/sOb62qV15mHb4qyDwTFMV5VqCU5FtnSpXkO5YVQEnemWWVrS/Os9r8gkBQ5NnhcPr+nGUBYXX2UJYGgfIopWXlvPX9AV78Zg/+Pl48enVnbuwTo3sHygqE3BTH7YTjllLJ/XFrr+ZcXr4QFGUFR1CUdQuOOvP43HVu1MGuQaA80v7UXGYu3sb6gxkMbhvJP67vRouIQLvLUnWBMdbeyFmBcQLyUh23NGtdXpo1WGBpYeXv4x9aIRgiKwmMCssahIOX866L0SBQHqu83PD++sM8s3QX5Qb+NLoDtw2Kw9udpsZUdZsx1mGn0yFR4ZZ77rI0yE+n0s508YLARlYonLo/dy8jupN1tfhF0CBQHu9IVgF//ngbK39OpWeLMJ6d0J0OTfQqW2WD8jLIz4D8tLMDorL7/DRrz+SUy+6HkU9e1GY1CJTCmhrzs5+O8uTnOzlZUMLdw9pwz6/a4u+jw1QoN1ZaZO1F5KVah48ucrRZDQKlKsjIK+bpL3by8eYjtImyhqmIj9NhKlT9Vl0QeOh4vsqTRQT58eJNPZlze18KS8q54T9reeyT7eQUlthdmlK20CBQHmtYh2iW/eFyfn1ZK+b9eIhRL67i210n7C5LKZfTIFAeLcjfh8ev6cx/fzuIhgG+TJ+bwL0fbCI1p+j8L1aqntAgUAroFRvO578bzB9HtmfZjhOMeOF/LNqYTF3rQ1PqYmgQKOXg5+PFfcPbsfT3g2kXHcyfPvqJW2evJykj3+7SlHIqDQKlztE2OoSFvxnI09d2ZfPhLEa9uIq3Vu+ntKzKGVSVqtOcGgQiMkZEfhaRRBGZWUWbiSKyU0R2iMgHzqxHqZry8hKmDmjJsj9czqA2jfjrkl1c//oa1iSm6eEiVe84c6pKb6ypKkcCyVhTVU4yxuys0KYdsBD4lTEmU0Siq5uvGPQ6AuV6xhiWbDvGU5/vJCWniB4twvjt0DaM6twYLx2qQtURdl1H0A9INMbsN8YUAx8C489pcyfwqjEmE+B8IaCUHUSEq7s3Y9VDV/D367qRlV/MXfM2MuLF/7EwIYniUj1kpOo2ZwZBcyCpwvNkx7KK2gPtReQHEVknImMqeyMRmSEiCSKSkJqa6qRylapegK83k/vH8t0Dw/jXpF4E+Hjz0KKtXP5/K3hr9X7yikrtLlGpi2J3Z7EP0A4YBkwC3hSRsHMbGWNmGWPijTHxUVFRLi5RqbN5ewnX9GjGkvsGM/fX/YiLDOSvS3Yx6JnveOGbPWTkVTKOvVJuzJnzsx0BWlR4HuNYVlEy8KMxpgQ4ICJ7sIJhgxPrUqpWiAhD20cxtH0Umw5n8p+V+3jl2728uWo/N/drwR1DWtM8rIHdZSp1Xs7sLPbB6iwejhUAG4DJxpgdFdqMwepAniYikcBmoKcxJr2q99XOYuXO9p7I4Y1V+/lks/U3z/iezblraGvaNdYhr5W9bBt9VESuBF4CvIHZxpi/ichTQIIx5jOx5g78JzAGKAP+Zoz5sLr31CBQdcGRrALeWr2fD9cnUVBSxsjOjfntsDb0jg23uzTloXQYaqVskpFXzNw1B5m79iBZ+SX0ig1j2sA4ruzWFD8fu7volCfRIFDKZnlFpSxMSOLdtYc4kJZHZLA/k/u1YHL/ljQJDbC7POUBNAiUchPl5YbViWm8u+Yg3/2cgrcIo7s2YdrAOPrGhWMdLVWq9lUXBM48a0gpdQ4vrzNnGh1Oz2fej4dYsCGJJVuP0bFJCNMGxTG+ZzMC/fRHU7mO7hEoZbOC4jI+3XKEOWsOsvt4Dg0DfLipbwumDogjtlGg3eWpekIPDSlVBxhjSDiUyZw1B/l6+3HKjOGKDtFMGxTHkLaROq6RuiR6aEipOkBE6BsXQd+4CI5nF/LB+sN88ONhps1eT6vIIKYOaMmE3jGEBvraXaqqZ3SPQCk3VlxazpfbjzF3zUE2Hc7C38eLsV2bcFPfWAa0jtDOZVVjemhIqXpg+5FsFmxI4pMtR8gpLCWuUSAT+7bght4xRDfUU1BV9TQIlKpHCorL+HL7MT7ckMT6Axl4ewlXdIjm5r4tGNYhCh9vvVBN/ZIGgVL11P7UXBYmJLNoYzJpuUVEh/hzY3wME+Nb0LJRkN3lKTeiQaBUPVdSVs53u1NYsCGJlT+nUG5gYOtG3NyvBaO7NCHA19vuEpXNNAiU8iDHsgtYlJDMgoQkkjMLCG3gy3W9mjMxvgWdmzW0uzxlEw0CpTxQeblh7f50PtyQxNfbj1NcVk7HJiGM79mca3o0JSZcL1bzJBoESnm4zLxiPtlyhE+3HGVLUhYAfePCGdezOVd2bUKjYH+bK1TOpkGglDrtUHoen/90lE+3HGVvSi7eXsKQdpGM79mMkZ2bEOyv15nWR3ZOTDMGeBlrYpq3jDHPVNFuArAI6GuMqfa3vAaBUrXDGMPu4zl8uuUon/90lCNZBQT4ejG8U2PG92jG0A5R+PtoJ3N9YUsQiIg31lSVI7HmJt6ANS3lznPahQBLAD/gXg0CpVyvvNyw8XAmn205ypJtx8jIK6ZhgA9juzZlfM9m9G/dCG8d66hOs2usoX5AojFmv6OID4HxwM5z2j0NPAs86MRalFLV8PI6M87R49d05vvEND7fcpQvth5lQUIS0SH+XN29Gdf0aErPFmE6tEU948wgaA4kVXieDPSv2EBEegMtjDFLRESDQCk34OvtxRUdormiQzQFxWV8tzuFT7ccYd66Q8z+4QDNwxpwdfemXN29GV2bN9RQqAds6xUSES/gBeC2GrSdAcwAiI2NdW5hSqnTGvh5c1X3plzVvSnZBSV8s/MEX2w9ytvfH+CNVftp2SiQq7pZ6zs31VCoq5zZRzAQeMIYM9rx/BEAY8w/HM9DgX1AruMlTYAMYFx1/QTaR6CU/bLyi/l6x3G+2HqMNfvSKSs3tI4M4irHnkL7xsEaCm7Grs5iH6zO4uHAEazO4snGmB1VtF8J/Ek7i5WqW9Jzi/h6h7WnsG5/OuUG2kYHOw4fNaVtdIjdJSps6iw2xpSKyL3A11inj842xuwQkaeABGPMZ87atlLKdRoF+zO5fyyT+8eSmlPEV9uP8cXWY7z87V5eWr6Xjk1CTh8+ahUZpHsKbkgvKFNKOcWJk4V8uc0KhYRDmQAE+/sQE96AFhGB1n144JnHEYF6MZsT6ZXFSilbHcsuYPnOE+xLzSM5M5+kjAKSMvPJLy47q114oC8x4YG0iLBCIia8ATERgacf6yiqF0/nLFZK2appaAOmDow7a5kxhsz8EpIy8knKzCc5s8DxuIDdx3JYviuF4tLys17TqWlDhrSLZHDbSPq1itBgqCW6R6CUckvl5YbU3CKSMqyQOJCWx48H0tl4KJOSMoOfjxd948K5rG0kQ9pG0aVZQ7z06ucq6aEhpVS9kV9cyvoDGXy/N43vE9PYfTwHsA4rDWoTyWDHHkOLCB1muyI9NKSUqjcC/XwY1iGaYR2iAUjJKWRNYjqr96bxfWIqS7YdA6Blo0AGt41kSLtIBraOJDTQ186y3ZruESil6g1jDPtSc1m9N40fEtNYuy+dvOIyvAS6xYQxoHUEfVtG0KdlOOFBfnaX61J6aEgp5ZFKysrZkpTF945g+Ck5i5Iy63de2+hg+saFE9/SGmyvRUSDen2NgwaBUkoBhSVlbE3OZsPBDBIOZrDxUCYnC0sBiA7xp2+ctbfQNy6CTk1D8PH2srni2qN9BEopBQT4etOvVQT9WkUA1plJe1NyTwfDhoOZp/sYAv286R0bTrxjr6FXbBhB9fSCN90jUEqpCo5lF5BwMPN0MOw6fhJjwNtLiGsUSLvoENo3DqZtY+u+VWRQnZjJTfcIlFKqhpqGNuCaHg24pkczAE4WlrD5cBYbD2aw+3gOe07ksGznccodf0N7CcQ1CqJd42DaRYecvm8dFVRnLnjTIFBKqWo0DPBlaPsohraPOr2ssKSMA2l57E3JZe+JHPaeyGVvinU1dJkjIbwEYiMCadc4hHbRwbRrHEyz0AZEhfgTGeJPiL+P23ROaxAopdQFCvD1plPThnRq2vCs5cWl5Y6AOBMOe0/ksmJ3CqXlZx+G9/fxIjLY3woGx31UsN9Zz0/dO7tvQoNAKaVqiZ+PFx2ahNChydlzMJSUlXMoPY/j2UWk5haSllNMam4RaTlFpOYWkZyZz5akTNLziqms27aBrzeRIX7cOiCOOy9vXet1axAopZST+Xp70TY65LyT9JSWlZORX0xaTjFpuUWk5hSddR/d0N8p9WkQKKWUm/Dx9iI6JIDokACXbtepV0uIyBgR+VlEEkVkZiXr/ygiO0Vkq4h8KyItnVmPUkqpX3JaEIiIN/AqMBboDEwSkc7nNNsMxBtjugOLgP9zVj1KKaUq58w9gn5AojFmvzGmGPgQGF+xgTFmhTEm3/F0HRDjxHqUUkpVwplB0BxIqvA82bGsKtOBLytbISIzRCRBRBJSU1NrsUSllFJuMaKSiEwB4oHnKltvjJlljIk3xsRHRUVV1kQppdRFcuZZQ0eAFhWexziWnUVERgB/BoYaY4qcWI9SSqlKOHOPYAPQTkRaiYgfcDPwWcUGItILeAMYZ4xJcWItSimlquC0IDDGlAL3Al8Du4CFxpgdIvKUiIxzNHsOCAY+EpEtIvJZFW+nlFLKSercMNQikgocusiXRwJptVhObXP3+sD9a9T6Lo3Wd2ncub6WxphKO1nrXBBcChFJqGo8bnfg7vWB+9eo9V0are/SuHt9VXGLs4aUUkrZR4NAKaU8nKcFwSy7CzgPd68P3L9Gre/SaH2Xxt3rq5RH9REopZT6JU/bI1BKKXUODQKllPJw9TIIajAPgr+ILHCs/1FE4lxYWwsRWeGYh2GHiPy+kjbDRCTbcZHdFhF53FX1ObZ/UES2ObadUMl6EZFXHJ/fVhHp7cLaOlT4XLaIyEkRuf+cNi7//ERktoikiMj2CssiROQbEdnruA+v4rXTHG32isg0F9b3nIjsdvwffiwiYVW8ttrvgxPre0JEjlT4f7yyitdW+/PuxPoWVKjtoIhsqeK1Tv/8Lpkxpl7dAG9gH9Aa8AN+Ajqf0+Zu4D+OxzcDC1xYX1Ogt+NxCLCnkvqGAV/Y+BkeBCKrWX8l1kixAgwAfrTx//o41oUytn5+wOVAb2B7hWX/B8x0PJ4JPFvJ6yKA/Y77cMfjcBfVNwrwcTx+trL6avJ9cGJ9TwB/qsF3oNqfd2fVd876fwKP2/X5XeqtPu4RnHceBMfzuY7Hi4DhIiKuKM4Yc8wYs8nxOAdr+I3qhud2R+OBd41lHRAmIk1tqGM4sM8Yc7FXmtcaY8wqIOOcxRW/Z3OBayt56WjgG2NMhjEmE/gGGOOK+owxy4w1FAzYPB9IFZ9fTdTk5/2SVVef43fHRGB+bW/XVepjENRkHoTTbRw/CNlAI5dUV4HjkFQv4MdKVg8UkZ9E5EsR6eLSwsAAy0Rko4jMqGT9hc414Sw3U/UPn52f3ymNjTHHHI+PA40raeMun+WvqWI+EM7/fXCmex2HrmZXcWjNHT6/IcAJY8zeKtbb+fnVSH0MgjpBRIKBxcD9xpiT56zehHW4owfwL+ATF5c32BjTG2ua0XtE5HIXb/+8HCPajgM+qmS13Z/fLxjrGIFbnqstIn8GSoH3q2hi1/fhdaAN0BM4hnX4xR1Novq9Abf/eaqPQVCTeRBOtxERHyAUSHdJddY2fbFC4H1jzH/PXW+MOWmMyXU8Xgr4ikikq+ozxhxx3KcAH2PtfldUo7kmnGwssMkYc+LcFXZ/fhWcOHXIzHFf2VDrtn6WInIbcDVwiyOsfqEG3wenMMacMMaUGWPKgTer2K7dn58PcD2woKo2dn1+F6I+BsF550FwPD91dsYNwHdV/RDUNsfxxLeBXcaYF6po0+RUn4WI9MP6f3JJUIlIkIiEnHqM1aG4/ZxmnwG3Os4eGgBkVzgE4ipV/hVm5+d3jorfs2nAp5W0+RoYJSLhjkMfoxzLnE5ExgAPYc0Hkl9Fm5p8H5xVX8V+p+uq2G5Nft6daQSw2xiTXNlKOz+/C2J3b7UzblhntezBOpvgz45lT2F94QECsA4pJALrgdYurG0w1iGCrcAWx+1K4C7gLkebe4EdWGdArAMGubC+1o7t/uSo4dTnV7E+AV51fL7bgHgX//8GYf1iD62wzNbPDyuUjgElWMepp2P1O30L7AWWAxGOtvHAWxVe+2vHdzERuN2F9SViHV8/9T08dSZdM2Bpdd8HF9X3nuP7tRXrl3vTc+tzPP/Fz7sr6nMsn3Pqe1ehrcs/v0u96RATSinl4erjoSGllFIXQINAKaU8nAaBUkp5OA0CpZTycBoESinl4TQIlHIhx8ioX9hdh1IVaRAopZSH0yBQqhIiMkVE1jvGkH9DRLxFJFdEXhRrHolvRSTK0baniKyrMK5/uGN5WxFZ7hj8bpOItHG8fbCILHLMBfC+q0a+VaoqGgRKnUNEOgE3AZcZY3oCZcAtWFc0JxhjugD/A/7ieMm7wMPGmO5YV8KeWv4+8KqxBr8bhHVlKlgjzt4PdMa68vQyp/+jlKqGj90FKOWGhgN9gA2OP9YbYA0YV86ZwcXmAf8VkVAgzBjzP8fyucBHjvFlmhtjPgYwxhQCON5vvXGMTeOY1SoO+N75/yylKqdBoNQvCTDXGPPIWQtFHjun3cWOz1JU4XEZ+nOobKaHhpT6pW+BG0QkGk7PPdwS6+flBkebycD3xphsIFNEhjiWTwX+Z6zZ55JF5FrHe/iLSKBL/xVK1ZD+JaLUOYwxO0XkUaxZpbywRpy8B8gD+jnWpWD1I4A1xPR/HL/o9wO3O5ZPBd4Qkacc73GjC/8ZStWYjj6qVA2JSK4xJtjuOpSqbXpoSCmlPJzuESillIfTPQKllPJwGgRKKeXhNAiUUsrDaRAopZSH0yBQSikP9/8B0X56fy6keTAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have trained the model we can evaluate on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.6868\n",
      "\tacc: 0.8064\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions themselves for all nodes using another node iterator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = node_data.index\n",
    "all_mapper = generator.flow(all_nodes)\n",
    "all_predictions = model.predict_generator(all_mapper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_predictions = target_encoding.inverse_transform(all_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31336</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061127</th>\n",
       "      <td>subject=Rule_Learning</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106406</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13195</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37879</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126012</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107140</th>\n",
       "      <td>subject=Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102850</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31349</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106418</th>\n",
       "      <td>subject=Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Predicted                    True\n",
       "31336           subject=Neural_Networks         Neural_Networks\n",
       "1061127           subject=Rule_Learning           Rule_Learning\n",
       "1106406  subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "13195    subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "37879     subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "1126012   subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "1107140                  subject=Theory                  Theory\n",
       "1102850         subject=Neural_Networks         Neural_Networks\n",
       "31349           subject=Neural_Networks         Neural_Networks\n",
       "1106418                  subject=Theory                  Theory"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n",
    "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data[\"subject\"]})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n",
    "    Gnx.nodes[nid][\"subject\"] = true\n",
    "    Gnx.nodes[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also add isTrain and isCorrect node attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nid in train_data.index:\n",
    "    Gnx.nodes[nid][\"isTrain\"] = True\n",
    "\n",
    "for nid in test_data.index:\n",
    "    Gnx.nodes[nid][\"isTrain\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nid in Gnx.nodes():\n",
    "    Gnx.nodes[nid][\"isCorrect\"] = (\n",
    "        Gnx.nodes[nid][\"subject\"] == Gnx.nodes[nid][\"PREDICTED_subject\"]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save in GraphML format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_fname = \"pred_n={}.graphml\".format(num_samples)\n",
    "nx.write_graphml(Gnx, os.path.join(dataset.data_directory, pred_fname))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings\n",
    "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n",
    "\n",
    "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = Model(inputs=x_inp, outputs=x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708, 32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict_generator(all_mapper)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb\n",
    "y = np.argmax(\n",
    "    target_encoding.transform(node_data[[\"subject\"]].to_dict(\"records\")), axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=node_data.index)\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GraphSAGE embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "plt.show()"
   ]
  }
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